Presentation is loading. Please wait.

Presentation is loading. Please wait.

University of Connecticut Automated Counterfeit IC Physical Defect Characterization Team 176 Wesley Stevens Dan Guerrera Ryan Nesbit Advisors: Professor.

Similar presentations


Presentation on theme: "University of Connecticut Automated Counterfeit IC Physical Defect Characterization Team 176 Wesley Stevens Dan Guerrera Ryan Nesbit Advisors: Professor."— Presentation transcript:

1 University of Connecticut Automated Counterfeit IC Physical Defect Characterization Team 176 Wesley Stevens Dan Guerrera Ryan Nesbit Advisors: Professor Mohammad Tehranipoor Professor Domenic Forte Electrical and Computer Engineering

2 All Rights Reserved 2 Summary Automated system for identifying physical defects Analyze various images Return relevant data regarding counterfeit status

3 All Rights Reserved 3 Project Overview Three main steps Acquire images of suspect and/or “golden” ICs Run different algorithms based on image location Algorithms return altered images with highlighted defects Uses images from different locations to find defects Leads/Pins – scratches, bends, corrosion Surface – scratches, discoloration, pattern variation Markings/Text – missing, faded, different location

4 All Rights Reserved 4 Project Overview

5 All Rights Reserved 5 Image Capture Locations Overall Top/bottom, sides, corners Features (Markings) Text (part number), Manufacturer Mark Surface Samples of main surfaces (top, bottom, sides) Edges (interface between top and bottom) Leads/Pins Top and bottom Connection with package

6 All Rights Reserved 6 Algorithms Binary Transformations Text recognition Ghost markings, extraneous markings Scratch detection Compare binary to structuring elements Statistical Analysis Texture comparison Scratches, color variation, different pattern, corrosion, contamination, package damage Feature matching Image alignment

7 All Rights Reserved 7 Binary Transformations

8 All Rights Reserved 8 Binary Transformations

9 All Rights Reserved 9 Object Counting We can use the number of objects in an image It can provide us with an indicator of defects.

10 All Rights Reserved 10 Scratch Detection

11 All Rights Reserved 11 Statistical Analysis

12 All Rights Reserved 12 Feature Matching

13 All Rights Reserved 13 User Results General algorithm results Pass or Fail List of all detected defects Image highlighting detected defects Summary Authentic/Suspect/Counterfeit

14 All Rights Reserved 14 User Results

15 All Rights Reserved 15 Project Plan Integrate as many defects as possible Finalize Golden IC comparison analysis Refine summary and analysis of results Confidence level for counterfeit


Download ppt "University of Connecticut Automated Counterfeit IC Physical Defect Characterization Team 176 Wesley Stevens Dan Guerrera Ryan Nesbit Advisors: Professor."

Similar presentations


Ads by Google